Statistical discrimination (economics)

Statistical discrimination is an economic theory of racial or gender inequality based on stereotypes . According to this theory, inequality may exist and persist between demographic groups even when economic agents (consumers, workers, employers, etc.) are rational and non-prejudiced. This type of treatment is labeled “statistical” because stereotypes may be based on the discriminated group’s average behavior.

The theory was pioneered by Kenneth Arrow and Edmund Phelps [1] The theory posits that in the absence of direct information about a certain fact of ability, a decision maker would substitute group averages. For instance, labor market discrimination exist Because May Employers do not know with certainty workers ‘Ability, therefore They May resort to basing employment decisions on the workers’ visible features, Such As group identity, as long as thesis features correlate With Some desirable goal more difficult to measure trait. The result is that atypical individuals from the disadvantage group. [2] This type of discrimination can result in a self-reinforcing vicious circle(3) improving their skills (their) on the market, [3] or improving their skills (their) on the market (education, etc.) is less than the non-discriminated group. [4]

A related form of (theorized) statistical discrimination is based on group variances , assuming equal averages. The decision maker needs to be risk averse ; such a decision maker will prefer the group with the lower variance. [5]Even assuming two theoretically identical group distributions, a risk averse decision maker will prefer the group for which a measurement (test) exists that minimizes the error term . [5]For example, if two groups, A and B, are theoretically identical, but a group is more likely to group B, then if two people, one from the other, and one from the other, using statistical discrimination, group B’s group score as more likely to be luck. Conversely, if the two groups are below average, it is a better estimate.

Statistical Discrimination is often used and tolerated, for example, when older people are required for life insurance, or when a college graduate is required for a job (because it is believed that college graduates perform, on average, better). Some well-known instances of statistical discrimination also exist. For example, many countries allow auto insurance companies to charge men and women with identical driving records (or factor in gender when deciding whether to deny coverage). The same society may not tolerate statistical discrimination when it is applied to protected groups. For example, it has been suggested that home mortgage lending discrimination against African Americans , which is illegal in theUnited States , may be caused by statistical discrimination. [6]

Market forces are expected to penalize some forms of statistical discrimination; for example, a company that is capable of doing business only in the field of employment. [7] However, this assumption does not take into account the economic cost of testing itself, which may not be possible in some scenarios like predicting the future likelihood of an employee’s quit for personal reasons. [8]